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R is a software environment for data manipulation, statistical computing as well as
graphical display of data and data analysis.
Among its greatest usages are statistical computing as well as machine learning
and deep learning.
R is extensively used in Academic, Healthcare and Finance and Insurance sectors.
R is in use in large companies such as Uber, Google, Airbnb, Facebook etc in their
very complex analysis and algorithms.
Since R is thoroughly backed by the academicians and researchers, most of the
new statistical functions are added to R as soon as the new concepts are in use.
R - 5 Exciting Facts
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The Originators And Developers
George Ross Ihaka.
Statistician based out of
New Zealand.
Current status: Retired
Associate Professor of Statistics,
University of Auckland since 2017.
Robert Clifford Gentleman.
Statistician and Bioinformatician
based out of Canada.
Current status: Vice President,
Computational Biology
at 23andMe.
R is currently developed by the R Development Core Team
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R is is freely available under the GNU General Public License. Most of the packages
or libraries in R are contributed by academicians, researchers and industry experts.
There are about 13 thousand libraries in R which are focused at helping data
scientists in data analysis, statistical modelling, time series analysis or for writing
machine learning algorithms.
R is enriched with large number of graphical functionalities. R gives an enormous
support to statisticians and data scientists with graphical display functionalities.
DID YOU KNOW: R’s ggplot2 is the most downloaded graphical package in the world.
R For Data Scientists
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The R Programming Language
R software environment has its own programming language which is also called R.
R software environment is written primarily in C, Fortran and in R itself.
R is an object oriented programming language.
R is an interpreted computer language.
DID YOU KNOW: You can call R functions from other programming languages such as C, C++,
Java, Python etc as well as call functions written in other languages from R. R also allows you to
interact with system commands therefore giving access to the operating systems.
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✓ Strengths
✓ Free and Open Source
✓ Strong User Community
✓ Highly extensible, flexible
✓ Implementation of high end statistical methods
✓ Flexible graphics and intelligent defaults
- Weakness
- Steep learning curve
- Preprocessing of data and slow for large datasets
R - Strengths and Weaknesses
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Objective: Generating the best value for advertisers on their marketing investment.
- Google has used R to fit autoregressive models to predict economic activity in
retail sales, automotive sales, home sales, and passenger arrival data. This forms
a huge live database for targeted marketing.
- Google also uses R for in-ad analysis by collecting data from set-top boxes. R
helps them determine number of viewers for each ad, how many viewers stay on-
channel, how many switch over etc.
- This information works as a base database which helps them measure
effectiveness of ads; helps them make online marketing more effective.
Google also uses R for visualization.
R Use Case 1 - Google
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R Use Case 2 - Facebook
Facebook uses R to
- Analyse status updates.
- Predict colleague interaction.
- Draw and design charts, social network
graph using ggplot2.
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R Use Case 3 - ANZ Bank
Objective: Assessing loss in mortgage
One of the largest banks in Australia, ANZ Bank has used R to fit models for
mortgage loss. R gave them flexibility to adapt to new modelling situations and
generate summaries, predictions etc.
Many banks now use R to interpret huge data to fit models for many small to large
size business cases.
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R Use Case 4 - John Deere (a farming
equipment manufacturer)
Objective: Predicting demand and
supply in the context of sales.
John Deere uses R for forecasting
demand for equipment, to
forecasting crop yields.
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R Use Case 5 - Weather Predictions
Objective: Weather forecasting in the US
In the USA, R is used in National weather service for weather predictions, hurricane
forecasting, forecasting of river flooding etc. R generates graphics representing real-
time hydrologic ensemble (probabilistic) forecasts.
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✓ Understand different data analysis problems and algorithms.
✓ Get a structured approach to convert high level data analytics problem
statements into well defined workflow for solutions.
✓ Learn how to take a problem statement and break it down into smaller
components and solve using an appropriate algorithm.
✓ Learn how to execute data science projects in one of the widely adopted
languages for data science, that is R.
✓ Linear algebra that is critical for data science algorithms.
✓ Fundamentals of statistics that is the backbone of data science.
Certificate Masterclass in Data Science
with R – What will you learn?
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70 Interactive Videos with
illustrative examples
24/7 Access to chat and
career support
12
Live Classes With
Instructors to learn
concepts first-hand
50 Quizzes to test your
learning on the go
25 Hours Of Lecture
20 Practice sessions and
real-life projects
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Learn At Your Own Pace With Integrated
Instructor Support
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✓ Certificate Of Completion.
✓ Integrated live projects and performance based internship.
✓ Membership of Spotle AI Alumni club with priority
access to courses, events and mentoring.
✓ Optimized career path fitment through AI.
✓ Personalised career
guidance and assistance.
Sign up for the Masterclass: https://spotle.ai/learn/data-science-with-r
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Your Take - aways
